PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a GEMM, applies Group Normalization, and then HardTanh.
    """
    def __init__(self, in_features, out_features, num_groups, hardtanh_min, hardtanh_max):
        super(Model, self).__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.group_norm = nn.GroupNorm(num_groups, out_features)
        self.hardtanh = nn.Hardtanh(min_val=hardtanh_min, max_val=hardtanh_max)

    def forward(self, x):
        """
        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_features).
        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_features).
        """
        x = self.gemm(x)
        x = self.group_norm(x)
        x = self.hardtanh(x)
        return x

batch_size = 1024
in_features = 8192
out_features = 8192
num_groups = 16
hardtanh_min = -2.0
hardtanh_max = 2.0

def get_inputs():
    return [torch.rand(batch_size, in_features)]

def get_init_inputs():
    return [in_features, out_features, num_groups, hardtanh_min, hardtanh_max]